It seems everyone is beginning to take climate change seriously. Not only humans, even robots are realising that we soon act on it. The world's first robot citizen'Sophia' attended the International Round Square Conference in Indore, where it talked about climate change, conservation of energy and sustainable development. "The governments of all the countries of the world need to change both their policy and ideas," Sophia said addressing an event called'Conversation with Humans' at Emerald Heights International School here. The event was attended by over a thousand students and several dignitaries.

In 2012, Geoffrey Hinton's research team used only two NVIDIA GPUs to train AlexNet, the revolutionary network architecture that handily won the ImageNet Large Scale Visual Recognition Challenge. It probably never occurred to these groundbreaking researchers that just seven years later, a new team of researchers would use almost 10,000 times more GPUs to train their AI model. A research team from NVIDIA, Oak Ridge National Laboratory (ORNL), and Uber has introduced new techniques that enabled them to train a fully convolutional neural network on the world's fastest supercomputer, Summit, with up to 27,600 NVIDIA GPUs. They managed to achieve an impressive, near-linear scaling of 0.93 on distributed training and produce a model capable of atomically-accurate reconstruction of materials -- a longstanding scientific problem involving materials imaging. In June 2018 the US Department of Energy's Oak Ridge National Laboratory in Tennessee unveiled the world's fastest supercomputer Summit, whosecomputing power reaches 200 petaflops.

The Conference Board of Canada's Global Commerce Centre (GCC) held a strategic foresight workshop on November 19, 2018. The workshop allowed GCC stakeholders to discuss and develop a series of plausible futures with specific assumptions on AI (artificial intelligence) global adoption, and the openness of the global trade environment. This strategic foresight report identifies four potential futures for consideration. The report provides insights into the challenges and opportunities that industries, the government, and the public may face as AI technologies and global economic trends continue to evolve. All four workshop groups highlighted the role of government policies and the need for good governance, ethical frameworks, and educational programs.

The C-130 Hercules is the most versatile aircraft in aviation history. From landing at the world's highest airstrip in the Himalayas to taking off and landing on an aircraft carrier in the middle of the Atlantic Ocean, the aircraft is celebrated for its unsurpassed versatility, performance and mission effectiveness. Today, 70 countries rely on the C-130 for search and rescue, peacekeeping, medical evacuations, scientific research, military operations, aerial refueling and humanitarian relief. More than 2,500 C-130s have been produced to date. The worldwide operational fleet includes legacy C-130 models as well as the current production variant – the C-130J Super Hercules.

A recent New York Times article fretting about the soaring costs of developing and training leading-edge deep learning models and my admittedly provocative Tweet questioning the premise and motives of the article's sources led to the type of online banter that indicates a nuanced question ill-suited for pithy Twitter responses. Fears of AI creating a chasm between haves and have-nots are common, however the topic of AI-fueled inequality typically centers on its economic effects, namely that the growing substitution of manual labor with algorithmic automation serves to further polarize income distributions as the knowledge class controlling and using the algorithms get richer while the working class being displaced by machines suffers. Many new technologies -- those we call'automation technologies' -- do not increase laborís productivity, but are explicitly aimed at replacing it by substituting cheaper capital (machines) in a range of tasks performed by humans. As a result, automation technologies always reduce the laborís share in value added (because they increase productivity by more than wages and employment). They may also reduce overall labor demand because they displace workers from the tasks they were previously performing.

NASA's InSight rover has provided the American space agency with weather reports, images and other interesting findings on Mars – but has struggled to probe its surface. In nearly eight months, the land rover has only dug through 14 inches of the red planet's surface, even though it was engineered to reach at least 16 feet to study how heat escapes from the interior. This blunder has come down to InSight's'mole' heat probe's inability to keep its footing in the soil – NASA believes the device is just bouncing in place. In nearly eight months, the land rover has only dug through 14 inches of the red planet's surface, even though it was engineered to reach at least 16 feet in order to study how heat escapes from the interior InSight, NASA's $1 billion rover, made landing on Mars in November 2018 after traveling through space for seven months. And although it has been a key player in the Mars mission, it has failed to explore the planet's interior.

--Machine learning models are widely adopted in scenarios that directly affect people. The development of software systems based on these models raises societal and legal concerns, as their decisions may lead to the unfair treatment of individuals based on attributes like race or gender . Data preparation is key in any machine learning pipeline, but its effect on fairness is yet to be studied in detail. In this paper, we evaluate how the fairness and effectiveness of the learned models are affected by the removal of the sensitive attribute, the encoding of the categorical attributes, and instance selection methods (including cross-validators and random undersampling). We used the Adult Income and the German Credit Data datasets, which are widely studied and known to have fairness concerns. We applied each data preparation technique individually to analyse the difference in predictive performance and fairness, using statistical parity difference, disparate impact, and the normalised prejudice index. The results show that fairness is affected by transformations made to the training data, particularly in imbalanced datasets. Removing the sensitive attribute is insufficient to eliminate all the unfairness in the predictions, as expected, but it is key to achieve fairer models. Additionally, the standard random undersampling with respect to the true labels is sometimes more prejudicial than performing no random undersampling. Software systems based on machine learning (ML) are being used at an increasingly higher rate and on a multitude of scenarios that have a significant impact on people's lives. Their ubiquity raises several legal and societal concerns, as decisions based on the output of ML models may introduce or perpetuate historical bias against some individuals, based on their intrinsic characteristics, such as race, gender or age. The use of automated decision-making systems is often appealing due to the gains associated with it, and might even be perceived as a step towards the eradication of personal bias from the process. Nevertheless, many are the risks associated with a careless adoption of decisions supported by these systems. In this context, fairness emerges as a key property in terms of the reliability and trustworthiness of software systems based on ML. These receive nowadays increased attention from regulatory institutions, with the recently approved European Union General Data Protection Regulation (GDPR) demanding organisations to handle personal data in a privacy-preserving, fair and transparent manner [1].

Separation of these tasks is the medium within the current state-of- the-art navigation methods. Each task is performed by an individual module and modularity is attained easily by this way. Nevertheless, modularity comes with the cost of possible incompatibility, especially with the presence of erroneous modules. An erroneous module in the pipeline could easily cause the other modules to fail as well. Therefore, in this work, the unification of these tasks is attempted within a single, reliable module using deep reinforcement learning (RL) [13]-[16].

After the Government's success to make Malta The Blockchain Island, we are now in a position to explore Artificial Intelligence as a new economic niche. Our vision is to replicate what we have done in the Blockchain sector and transform the potential of Artificial Intelligence into a new contributor to Malta's economic growth in digital innovation. The Government's aim is to develop a National AI Strategy and put Malta amongst the top 10 nations with a national strategy for Artificial Intelligence. Our objectives include having discussions on this subject with stakeholders to build awareness of the key topics and issues that will form a national AI Framework. We want to consult on a policy that considers for ethically aligned, transparent and socially responsible AI, identify policy, regulatory and fiscal measures to strengthen Malta's appeal as a hub for foreign investment in this sector, while identifying the underlying skill base and infrastructure needed to support AI.